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          RedisNoSQL的四大分类NoSQL = Not Only SQL，泛指非关系型数据库。
KV键值对
新浪(Redis)
美团(Redis + Tair)
阿里、百度(Redis + memecache)

文档型数据库
MongoDB —— MongoDB是一个基于分布式文件存储的数据库，C++编写，主要用来处理大量的文档。MongoDB是一个介于关系型数据库和非关系型数据库中间的产品。Mo
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          Spring优势
开源免费
轻量级、非入侵式的框架
控制反转IOC + 面向切面编程AOP
支持事务处理以及框架整合

总结：Spring就是一个轻量级的控制反转和面向切面编程的框架
Spring七大模块
核心容器（Spring Core）　　核心容器提供Spring框架的基本功能。Spring以bean的方式组织和管理Java应用中的各个组件及其关系。Spring使用BeanFactory来产生
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          MyBatis框架使用1. 导入mybatis的jar包&lt;dependency&gt;
    &lt;groupId&gt;org.mybatis&lt;/groupId&gt;
    &lt;artifactId&gt;mybatis&lt;/artifactId&gt;
    &lt;version&gt;3.5.2&lt;/version&gt;
&lt;/dependency&
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          <img src="/img/GIR.png" srcset="/img/loading.gif" alt="基于多知识库迭代检索的常识问答系统">
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        <a href="/2020/11/25/%E5%9F%BA%E4%BA%8E%E5%A4%9A%E7%9F%A5%E8%AF%86%E5%BA%93%E8%BF%AD%E4%BB%A3%E6%A3%80%E7%B4%A2%E7%9A%84%E5%B8%B8%E8%AF%86%E9%97%AE%E7%AD%94%E7%B3%BB%E7%BB%9F/">基于多知识库迭代检索的常识问答系统</a>
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        <a href="/2020/11/25/%E5%9F%BA%E4%BA%8E%E5%A4%9A%E7%9F%A5%E8%AF%86%E5%BA%93%E8%BF%AD%E4%BB%A3%E6%A3%80%E7%B4%A2%E7%9A%84%E5%B8%B8%E8%AF%86%E9%97%AE%E7%AD%94%E7%B3%BB%E7%BB%9F/">
          
          
            
          
          多知识库常识知识迭代检索论文地址

背景常识问答任务需要引入外部知识来帮助模型更好地理解自然语言问题，现有的解决方案大都采用两阶段框架：

第一阶段 —— 从广泛的知识来源中找到与给定问题相关的知识事实或者用预训练模型生成相关的知识
第二阶段 —— 将找到的或者生成的知识与问题融合以预测答案。

实验结果证明，外部知识融合到问答系统的做法是十分有效的，但这仍然存在一个关键的问题：就从单一外部知识库
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          <img src="/img/%E5%B8%B8%E8%AF%86%E9%97%AE%E7%AD%94.png" srcset="/img/loading.gif" alt="常识问答大比拼">
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        <a href="/2020/11/17/%E5%B8%B8%E8%AF%86%E9%97%AE%E7%AD%94%E5%A4%A7%E6%AF%94%E6%8B%BC/">常识问答大比拼</a>
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          不同的道路
不引入相关知识BaselineBERT + MLP + Softmax
BERT可以换成RoBERTa、XLNet或者ALBERT等。
Text2TextT5提出了一种文本到文本的范式，需要指定任务类型。

UnifiedQA使用单个QA系统，跨越不同QA数据集的格式障碍。虽然基于T5，但不需要指定任务类型。

引入相关知识ConceptNet + Wikipedia + XLNet 
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          《COMET：Commonsense Transformers for Automatic Knowledge Graph Construction》论文地址
论文源码
任务
目的层面 —— 根据两个当前最常用的常识知识图ATOMIC和ConceptNet构建一个用于开发常识知识的自适应生成模型COMET，以协助完成常识知识的自我补充。

​    COMET是一个自适应框架，用于通过在知识元组的
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          NLP任务
前处理任务前处理任务的结果可作为下游任务输入的额外特征。
POSTa(词性标注)往模型中输入句子，对每一个token进行词性的识别。
识别出的词性可以用于下游任务。

Word Segmentation(分词)对于英文，显然句子有天然的分词。所以分词通常是针对中文句子。
分词之后，模型的输入就可以以词汇作单位，而不再以字作单位。
以下面例子做说明：
将一个句子按字输入模型，训练模型来对
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          <img src="/img/%E6%BA%AF%E5%9B%A0%E6%8E%A8%E7%90%86.png" srcset="/img/loading.gif" alt="溯因推理">
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          Abductive Commonsense Reasoning(溯因推理)论文地址论文代码
介绍溯因推理是对不完全观察情境的最合理解释或假设的推论。

上图给出的是一个简明扼要的例子：
给定不同时间节点上的情境观测值 O_{1} 和 O_{2} ，溯因推理的任务是从给出的一众解释性假设 H_{1}, \space H_{2}, \space \cdots ,H_{n} 中选出最合理的。例如，上图在
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